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# Statistics skewness

I don’t need any references, just an answer for each of the six examples given. Please state whether
they are positively skewed, negatively skewed, or normal. And just a very brief explanation why
for each. Thank you!

Income:
Positively skewed: This is mainly because income is bounded below usually by minimum
income, with low income levels being plausible, yet very large maximum income are very well
known to occur (mostly in extents of magnitude higher values), with a significant number being
in the middle making the difference between minimum income and maximum income levels
extremely varied which is characterized by long tail on the positive side of the skew.

1. Mileage on used cars for sale:
Positively skewed: This is mainly because income is bounded below usually by minimum
mileages, with low mileage being plausible, yet very large maximum mileages are very well
known to occur (mostly in extents of magnitude higher levels), but most of the mileages lying
within the middle region of the distribution which is characterized by long tail on the positive
side of the skew.
2. House prices:
Positively skewed: This is mainly because income is bounded below usually by minimum prices,
with low prices being plausible, yet extremely high prices are very well known to occur, but a
significant number of prices being in the middle making the difference between minimum
income and maximum prices extremely varied which is characterized by long tail on the positive
side of the skew.
3. Scores on an easy test:
Normally distributed: This is attributed to the fact that an easy test provides a random measure of
studentsâ€™ knowledge in which a similar platform is universally applied meaning that there is no
outside influence to interfere with scores.
4. Scores on a test that students are motivated:
Positively skewed: This is because the students will tend to score higher due to the available
motivation which might be inform of reward attributable to the extra effort put by the students in
order to get the reward.
5. Age at death in developed countries:
Negatively skewed: This is because most national statistical agencies do not conclusively cover
the extreme old ages in their age bins, where for instance most of them go up to between 95> or
100> years yet there are older people making the final bin uncomfortably wide hence resulting to
negative skew.